Network capacity estimation based on centrality measures for building resilient road networks

Abstract

Estimating the capacity of a road network can be important to ensure resilience in emergency situations caused by earthquakes, especially during emergencies requiring rapid evacuation and recovery. However, an actual road network often consists of numerous nodes and links, and capacity estimation for such a large-scale road network poses computational challenges. Macroscopic methods of traffic analysis, e.g., solving the Lighthill-Whitham-Richards (LWR) and Payne-Whitham (PW) models, are known to provide relatively accurate results for traffic estimation, but the analysis may be computationally expensive. When considering uncertainty factors related to a road network and its constituent bridges, repeated capacity estimation based on such an approach is required for the target network, which may incur enormous computational costs. To overcome these challenges, this study introduces network centrality measures for the efficient capacity estimation of a large-scale road network. Several types of centrality measures have been developed so far. To compare them and select an appropriate measure for the desired capacity estimation in this study, the existing measures were applied to a relatively simple road network and compared the results with those obtained from a more sophisticated traffic analysis. For the task, Monte Carlo simulations were performed to consider various post-earthquake damage states of bridges within the network. As a result, the analysis results using betweenness centrality were found to be closer to the results of sophisticated traffic analysis than other centrality measures. Based on this finding, the betweenness centrality was applied to an actual large-scale road network in the Republic of Korea, and its capacity was estimated successfully. In addition, the proposed approach enabled probabilistically estimating the network capacity by considering various damage states of bridges with their seismic fragilities. It is believed that the proposed approach can be useful for rapid decision-making and resource allocation, enhancing resilience in disaster by identifying and prioritizing critical spots within road networks.